Fuzzy averaging filter for impulse noise reduction in colour images with a correction step

[EN] In this paper we propose a fuzzy detection and reduction method for impulse noise in colour images. Detection is based on the fuzzyfication of a well-known statistic called ROD. The noise degrees obtained are used to reduce impulses by employing a fuzzy averaging between the input colour vector and a robust estimate of noise-free colour vector within the input neighbourhood. Fuzzy averaging has some advantages in terms of both noise reduction and detail preservation in front of detect and replace approaches because of threshold based decisions of the latter. However, robustness of the former is lower. We solve this problem by including a correction mechanism that checks the fuzzy noise degree of the output and replaces it with a robust colour vector either when noise has not been properly reduced or when a colour artefact has been introduced. We carry out a thorough study of the method parameter setting and give a convenient and robust setting. Experimental results show that our approach is very robust in front of four different types of impulse noise.The authors are very grateful to the reviewers for their valuable suggestions. Valentin Gregori and Samuel Morillas acknowledges the support of Ministry of Economy and Competitiveness of Spain under grant MTM 2015-64373-P (MINECO/FEDER, UE). Bernardino Roig and Almanzor Sapena acknowledges the support of Generalitat Valencians under grant AICO/2017/059.Gregori Gregori, V.; Morillas, S.; Roig, B.; Sapena Piera, A. (2018). Fuzzy averaging filter for impulse noise reduction in colour images with a correction step. Journal of Visual Communication and Image Representation. 55:518-528. https://doi.org/10.1016/j.jvcir.2018.06.025S5185285

A simple fuzzy method to remove mixed Gaussian-Impulsive noise from color images

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Mixed impulsive and Gaussian noise reduction from digital color images is a challenging task because it is necessary to appropriately process both types of noise that in turn need to be distinguished from the original image structures such as edges and details. Fuzzy theory is useful to build simple, efficient, and effective solutions for this problem. In this paper, we propose a fuzzy method to reduce Gaussian and impulsive noise from color images. Our method uses one only filtering operation: a weighted averaging. A fuzzy rule system is used to assign the weights in the averaging so that both noise types are reduced and image structures are reserved. We provide experimental results to show that the performance of the method is competitive with respect to state-of-the-art filters.This work was supported by the Spanish Ministry of Science and Innovation under Grant MTM2009-12872-C02-01.Camarena Estruch, JG.; Gregori Gregori, V.; Morillas, S.; Sapena Piera, A. (2013). A simple fuzzy method to remove mixed Gaussian-Impulsive noise from color images. IEEE Transactions on Fuzzy Systems. 21(5):971-978. https://doi.org/10.1109/TFUZZ.2012.2234754S97197821

Fuzzy techniques for noise removal in image sequences and interval-valued fuzzy mathematical morphology

Image sequences play an important role in today's world. They provide us a lot of information. Videos are for example used for traffic observations, surveillance systems, autonomous navigation and so on. Due to bad acquisition, transmission or recording, the sequences are however usually corrupted by noise, which hampers the functioning of many image processing techniques. A preprocessing module to filter the images often becomes necessary. After an introduction to fuzzy set theory and image processing, in the first main part of the thesis, several fuzzy logic based video filters are proposed: one filter for grayscale video sequences corrupted by additive Gaussian noise and two color extensions of it and two grayscale filters and one color filter for sequences affected by the random valued impulse noise type. In the second main part of the thesis, interval-valued fuzzy mathematical morphology is studied. Mathematical morphology is a theory intended for the analysis of spatial structures that has found application in e.g. edge detection, object recognition, pattern recognition, image segmentation, image magnification… In the thesis, an overview is given of the evolution from binary mathematical morphology over the different grayscale morphology theories to interval-valued fuzzy mathematical morphology and the interval-valued image model. Additionally, the basic properties of the interval-valued fuzzy morphological operators are investigated. Next, also the decomposition of the interval-valued fuzzy morphological operators is investigated. We investigate the relationship between the cut of the result of such operator applied on an interval-valued image and structuring element and the result of the corresponding binary operator applied on the cut of the image and structuring element. These results are first of all interesting because they provide a link between interval-valued fuzzy mathematical morphology and binary mathematical morphology, but such conversion into binary operators also reduces the computation. Finally, also the reverse problem is tackled, i.e., the construction of interval-valued morphological operators from the binary ones. Using the results from a more general study in which the construction of an interval-valued fuzzy set from a nested family of crisp sets is constructed, increasing binary operators (e.g. the binary dilation) are extended to interval-valued fuzzy operators

Color Image Processing based on Graph Theory

[ES] La visión artificial es uno de los campos en mayor crecimiento en la actualidad que, junto con otras tecnologías como la Biometría o el Big Data, se ha convertido en el foco de interés de numerosas investigaciones y es considerada como una de las tecnologías del futuro. Este amplio campo abarca diversos métodos entre los que se encuentra el procesamiento y análisis de imágenes digitales. El éxito del análisis de imágenes y otras tareas de procesamiento de alto nivel, como pueden ser el reconocimiento de patrones o la visión 3D, dependerá en gran medida de la buena calidad de las imágenes de partida. Hoy en día existen multitud de factores que dañan las imágenes dificultando la obtención de imágenes de calidad óptima, esto ha convertido el (pre-) procesamiento digital de imágenes en un paso fundamental previo a la aplicación de cualquier otra tarea de procesado. Los factores más comunes son el ruido y las malas condiciones de adquisición: los artefactos provocados por el ruido dificultan la interpretación adecuada de la imagen y la adquisición en condiciones de iluminación o exposición deficientes, como escenas dinámicas, causan pérdida de información de la imagen que puede ser clave para ciertas tareas de procesamiento. Los pasos de (pre-)procesamiento de imágenes conocidos como suavizado y realce se aplican comúnmente para solventar estos problemas: El suavizado tiene por objeto reducir el ruido mientras que el realce se centra en mejorar o recuperar la información imprecisa o dañada. Con estos métodos conseguimos reparar información de los detalles y bordes de la imagen con una nitidez insuficiente o un contenido borroso que impide el (post-)procesamiento óptimo de la imagen. Existen numerosos métodos que suavizan el ruido de una imagen, sin embargo, en muchos casos el proceso de filtrado provoca emborronamiento en los bordes y detalles de la imagen. De igual manera podemos encontrar una enorme cantidad de técnicas de realce que intentan combatir las pérdidas de información, sin embargo, estas técnicas no contemplan la existencia de ruido en la imagen que procesan: ante una imagen ruidosa, cualquier técnica de realce provocará también un aumento del ruido. Aunque la idea intuitiva para solucionar este último caso será el previo filtrado y posterior realce, este enfoque ha demostrado no ser óptimo: el filtrado podrá eliminar información que, a su vez, podría no ser recuperable en el siguiente paso de realce. En la presente tesis doctoral se propone un modelo basado en teoría de grafos para el procesamiento de imágenes en color. En este modelo, se construye un grafo para cada píxel de tal manera que sus propiedades permiten caracterizar y clasificar dicho pixel. Como veremos, el modelo propuesto es robusto y capaz de adaptarse a una gran variedad de aplicaciones. En particular, aplicamos el modelo para crear nuevas soluciones a los dos problemas fundamentales del procesamiento de imágenes: suavizado y realce. Se ha estudiado el modelo en profundidad en función del umbral, parámetro clave que asegura la correcta clasificación de los píxeles de la imagen. Además, también se han estudiado las posibles características y posibilidades del modelo que nos han permitido sacarle el máximo partido en cada una de las posibles aplicaciones. Basado en este modelo se ha diseñado un filtro adaptativo capaz de eliminar ruido gaussiano de una imagen sin difuminar los bordes ni perder información de los detalles. Además, también ha permitido desarrollar un método capaz de realzar los bordes y detalles de una imagen al mismo tiempo que se suaviza el ruido presente en la misma. Esta aplicación simultánea consigue combinar dos operaciones opuestas por definición y superar así los inconvenientes presentados por el enfoque en dos etapas.[CA] La visió artificial és un dels camps en major creixement en l'actualitat que, junt amb altres tecnlogies com la Biometria o el Big Data, s'ha convertit en el focus d'interés de nombroses investigacions i és considerada com una de les tecnologies del futur. Aquest ampli camp comprén diversos m`etodes entre els quals es troba el processament digital d'imatges i anàlisis d'imatges digitals. L'èxit de l'anàlisis d'imatges i altres tasques de processament d'alt nivell, com poden ser el reconeixement de patrons o la visió 3D, dependrà en gran manera de la bona qualitat de les imatges de partida. Avui dia existeixen multitud de factors que danyen les imatges dificultant l'obtenció d'imatges de qualitat òptima, açò ha convertit el (pre-) processament digital d'imatges en un pas fonamental previa la l'aplicació de qualsevol altra tasca de processament. Els factors més comuns són el soroll i les males condicions d'adquisició: els artefactes provocats pel soroll dificulten la inter- pretació adequada de la imatge i l'adquisició en condicions d'il·luminació o exposició deficients, com a escenes dinàmiques, causen pèrdua d'informació de la imatge que pot ser clau per a certes tasques de processament. Els passos de (pre-) processament d'imatges coneguts com suavitzat i realç s'apliquen comunament per a resoldre aquests problemes: El suavitzat té com a objecte reduir el soroll mentres que el real se centra a millorar o recuperar la informació imprecisa o danyada. Amb aquests mètodes aconseguim reparar informació dels detalls i bords de la imatge amb una nitidesa insuficient o un contingut borrós que impedeix el (post-)processament òptim de la imatge. Existeixen nombrosos mètodes que suavitzen el soroll d'una imatge, no obstant això, en molts casos el procés de filtrat provoca emborronamiento en els bords i detalls de la imatge. De la mateixa manera podem trobar una enorme quantitat de tècniques de realç que intenten combatre les pèrdues d'informació, no obstant això, aquestes tècniques no contemplen l'existència de soroll en la imatge que processen: davant d'una image sorollosa, qualsevol tècnica de realç provocarà també un augment del soroll. Encara que la idea intuïtiva per a solucionar aquest últim cas seria el previ filtrat i posterior realç, aquest enfocament ha demostrat no ser òptim: el filtrat podria eliminar informació que, al seu torn, podria no ser recuperable en el seguënt pas de realç. En la present Tesi doctoral es proposa un model basat en teoria de grafs per al processament d'imatges en color. En aquest model, es construïx un graf per a cada píxel de tal manera que les seues propietats permeten caracteritzar i classificar el píxel en quëstió. Com veurem, el model proposat és robust i capaç d'adaptar-se a una gran varietat d'aplicacions. En particular, apliquem el model per a crear noves solucions als dos problemes fonamentals del processament d'imatges: suavitzat i realç. S'ha estudiat el model en profunditat en funció del llindar, paràmetre clau que assegura la correcta classificació dels píxels de la imatge. A més, també s'han estudiat les possibles característiques i possibilitats del model que ens han permés traure-li el màxim partit en cadascuna de les possibles aplicacions. Basat en aquest model s'ha dissenyat un filtre adaptatiu capaç d'eliminar soroll gaussià d'una imatge sense difuminar els bords ni perdre informació dels detalls. A més, també ha permés desenvolupar un mètode capaç de realçar els bords i detalls d'una imatge al mateix temps que se suavitza el soroll present en la mateixa. Aquesta aplicació simultània aconseguix combinar dues operacions oposades per definició i superar així els inconvenients presentats per l'enfocament en dues etapes.[EN] Computer vision is one of the fastest growing fields at present which, along with other technologies such as Biometrics or Big Data, has become the focus of interest of many research projects and it is considered one of the technologies of the future. This broad field includes a plethora of digital image processing and analysis tasks. To guarantee the success of image analysis and other high-level processing tasks as 3D imaging or pattern recognition, it is critical to improve the quality of the raw images acquired. Nowadays all images are affected by different factors that hinder the achievement of optimal image quality, making digital image processing a fundamental step prior to the application of any other practical application. The most common of these factors are noise and poor acquisition conditions: noise artefacts hamper proper image interpretation of the image; and acquisition in poor lighting or exposure conditions, such as dynamic scenes, causes loss of image information that can be key for certain processing tasks. Image (pre-) processing steps known as smoothing and sharpening are commonly applied to overcome these inconveniences: Smoothing is aimed at reducing noise and sharpening at improving or recovering imprecise or damaged information of image details and edges with insufficient sharpness or blurred content that prevents optimal image (post-)processing. There are many methods for smoothing the noise in an image, however in many cases the filtering process causes blurring at the edges and details of the image. Besides, there are also many sharpening techniques, which try to combat the loss of information due to blurring of image texture and need to contemplate the existence of noise in the image they process. When dealing with a noisy image, any sharpening technique may amplify the noise. Although the intuitive idea to solve this last case would be the previous filtering and later sharpening, this approach has proved not to be optimal: the filtering could remove information that, in turn, may not be recoverable in the later sharpening step. In the present PhD dissertation we propose a model based on graph theory for color image processing from a vector approach. In this model, a graph is built for each pixel in such a way that its features allow to characterize and classify the pixel. As we will show, the model we proposed is robust and versatile: potentially able to adapt to a variety of applications. In particular, we apply the model to create new solutions for the two fundamentals problems in image processing: smoothing and sharpening. To approach high performance image smoothing we use the proposed model to determine if a pixel belongs to a at region or not, taking into account the need to achieve a high-precision classification even in the presence of noise. Thus, we build an adaptive soft-switching filter by employing the pixel classification to combine the outputs from a filter with high smoothing capability and a softer one to smooth edge/detail regions. Further, another application of our model allows to use pixels characterization to successfully perform a simultaneous smoothing and sharpening of color images. In this way, we address one of the classical challenges within the image processing field. We compare all the image processing techniques proposed with other state-of-the-art methods to show that they are competitive both from an objective (numerical) and visual evaluation point of view.Pérez Benito, C. (2019). Color Image Processing based on Graph Theory [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/123955TESI

Novel Restoration Techniques for Images Corrupted with High Density Impulsive Noise

Impulse noise is a most common noise which affects the image quality during acquisition or transmission, reception or storage and retrieval process. Impulse noise comes under two categories: (1) fixed-valued impulse noise, also known as salt-and-pepper noise (SPN) due to its appearance, where the noise value may be either the minimum or maximum value of the dynamic gray-scale range of image and (2) random-valued impulse noise (RVIN), where the noisy pixel value is bounded by the range of the dynamic gray-scale of the image. In literature, many efficient filters are proposed to suppress the impulse noise. But their performance is not good under moderate and high noise conditions. Hence, there is sufficient scope to explore and develop efficient filters for suppressing the impulse noise at high noise densities. In the present research work, efforts are made to propose efficient filters that suppress the impulse noise and preserve the edges and fine details of an image in wide range of noise densities. It is clear from the literature that detection followed by filtering achieves better performance than filtering without detection. Hence, the proposed filters in this thesis are based on detection followed by filtering techniques. The filters which are proposed to suppress the SPN in this thesis are: Adaptive Noise Detection and Suppression (ANDS) Filter Robust Estimator based Impulse-Noise Reduction (REIR) Algorithm Impulse Denoising Using Improved Progressive Switching Median Filter (IDPSM) Impulse-Noise Removal by Impulse Classification (IRIC) A Novel Adaptive Switching Filter-I (ASF-I) for Suppression of High Density SPN A Novel Adaptive Switching Filter-II (ASF-II) for Suppression of High Density SPN Impulse Denoising Using Iterative Adaptive Switching Filter (IASF) In the first method, ANDS, neighborhood difference is employed for pixel classification. Controlled by binary image, the noise is filtered by estimating the value of a pixel with an adaptive switching based median filter applied exclusively to neighborhood pixels that are labeled noise-free. The proposed filter performs better in retaining edges and fine details of an image at low-to-medium densities of fixed-valued impulse noise.The REIR method is based on robust statistic technique, where adaptive window is used for pixel classification. The noisy pixel is replaced with Lorentzian estimator or average of the previously processed pixels. Because of adaptive windowing technique, the filter is able to suppress the noise at a density as high as 90%. In the proposed method, IDPSM, the noisy pixel is replaced with median of uncorrupted pixels in an adaptive filtering window. The iterative nature of the filter makes it more efficient in noise detection and adaptive filtering window technique makes it robust enough to preserve edges and fine details of an image in wide range of noise densities. The forth proposed method is IRIC. The noisy pixel is replaced with median of processed pixels in the filtering window. At high noise densities, the median filtering may not be able to reject outliers always. Under such circumstances, the processed left neighboring pixel is considered as the estimated output. The computational complexity of this method is equivalent to that of a median filter having a 3×3 window. The proposed algorithm requires simple physical realization structures. Therefore, this algorithm may be quite useful for online and real-time applications. Two different adaptive switching filters: ASF-I and ASF-II are developed for suppressing SPN at high noise density. The noisy pixel is replaced with alpha-trimmed mean value of uncorrupted pixels in the adaptive filtering window. Depending on noise estimation, a small filtering window size is initially selected and then the scheme adaptively changes the window size based on the number of noise-free pixels. Therefore, the proposed method removes the noise much more effectively even at noise density as high as 90% and yields high image quality. In the proposed method IASF, noisy pixel is replaced with alpha-trimmed mean value of uncorrupted pixels in the adaptive filtering window. Due to its iterative structure, the performance of this filter is better than existing order-statistic filters. Further, the adaptive filtering window makes it robust enough to preserve the edges and fine details of an image. Novel Restoration Techniques for Images Corrupted with High Density Impulsive Noise x The filters which are proposed for suppressing random-valued impulse noise (RVIN) are: Adaptive Window based Pixel-Wise MAD (AW-PWMAD) Algorithm Adaptive Local Thresholding with MAD (ALT-MAD) Algorithm The proposed method, Adaptive Window based Pixel-Wise MAD (AW-PWMAD) Algorithm is a modified MAD (Median of the Absolute Deviations from the median) scheme alongwith a threshold employed for pixel-classification. The noisy pixel is replaced with median of uncorrupted pixels in adaptive filtering window. Another proposed method for denoising the random-valued and fixed-valued impulse noise is ALT-MAD. A modified MAD based algorithm alongwith a local adaptive threshold is utilized for pixel-classification. The noisy pixel is replaced with median of uncorrupted pixels in the filtering window of adaptively varied size. Three threshold functions are suggested and employed in this algorithm. Thus, three different versions, namely, ALT-MAD-1, ALT-MAD-2 and ALT-MAD-3 are developed. They are observed to be quite efficient in noise detection and filtering. In the last part of the thesis, some efforts are made to develop filters for color image denoising. The filters which perform better in denoising gray-scale images are developed for suppression of impulsive noise from color images. Since the performance of denoising filters degrades in other color spaces, efforts are made to develop color image denoising filters in RGB color space only in this research work. The developed filters are: Multi-Channel Robust Estimator based Impulse-Noise Reduction (MC-REIR) Algorithm Multi-Channel Impulse-Noise Removal by Impulse Classification (MC-IRIC) Multi-Channel Iterative Adaptive Switching Filter (MC-IASF) Multi-Channel Adaptive Local Thresholding with MAD (MC-ALT-MAD) Algorithm It is observed from the simulation results that the proposed filters perform better than the existing methods. The proposed methods: ASF-1 and IASF exhibit quite superior performance in suppressing SPN in high noise densities compared to other methods. Similarly ALT-MAD-3 exhibits much better performance in suppressing RVIN of low to medium noise densities.The REIR method is based on robust statistic technique, where adaptive window is used for pixel classification. The noisy pixel is replaced with Lorentzian estimator or average of the previously processed pixels. Because of adaptive windowing technique, the filter is able to suppress the noise at a density as high as 90%. In the proposed method, IDPSM, the noisy pixel is replaced with median of uncorrupted pixels in an adaptive filtering window. The iterative nature of the filter makes it more efficient in noise detection and adaptive filtering window technique makes it robust enough to preserve edges and fine details of an image in wide range of noise densities. The forth proposed method is IRIC. The noisy pixel is replaced with median of processed pixels in the filtering window. At high noise densities, the median filtering may not be able to reject outliers always. Under such circumstances, the processed left neighboring pixel is considered as the estimated output. The computational complexity of this method is equivalent to that of a median filter having a 3×3 window. The proposed algorithm requires simple physical realization structures. Therefore, this algorithm may be quite useful for online and real-time applications. Two different adaptive switching filters: ASF-I and ASF-II are developed for suppressing SPN at high noise density. The noisy pixel is replaced with alpha-trimmed mean value of uncorrupted pixels in the adaptive filtering window. Depending on noise estimation, a small filtering window size is initially selected and then the scheme adaptively changes the window size based on the number of noise-free pixels. Therefore, the proposed method removes the noise much more effectively even at noise density as high as 90% and yields high image quality. In the proposed method IASF, noisy pixel is replaced with alpha-trimmed mean value of uncorrupted pixels in the adaptive filtering window. Due to its iterative structure, the performance of this filter is better than existing order-statistic filters. Further, the adaptive filtering window makes it robust enough to preserve the edges and fine details of an image. Novel Restoration Techniques for Images Corrupted with High Density Impulsive Noise x The filters which are proposed for suppressing random-valued impulse noise (RVIN) are: Adaptive Window based Pixel-Wise MAD (AW-PWMAD) Algorithm Adaptive Local Thresholding with MAD (ALT-MAD) Algorithm The proposed method, Adaptive Window based Pixel-Wise MAD (AW-PWMAD) Algorithm is a modified MAD (Median of the Absolute Deviations from the median) scheme alongwith a threshold employed for pixel-classification. The noisy pixel is replaced with median of uncorrupted pixels in adaptive filtering window. Another proposed method for denoising the random-valued and fixed-valued impulse noise is ALT-MAD. A modified MAD based algorithm alongwith a local adaptive threshold is utilized for pixel-classification. The noisy pixel is replaced with median of uncorrupted pixels in the filtering window of adaptively varied size. Three threshold functions are suggested and employed in this algorithm. Thus, three different versions, namely, ALT-MAD-1, ALT-MAD-2 and ALT-MAD-3 are developed. They are observed to be quite efficient in noise detection and filtering. In the last part of the thesis, some efforts are made to develop filters for color image denoising. The filters which perform better in denoising gray-scale images are developed for suppression of impulsive noise from color images. Since the performance of denoising filters degrades in other color spaces, efforts are made to develop color image denoising filters in RGB color space only in this research work. The developed filters are: Multi-Channel Robust Estimator based Impulse-Noise Reduction (MC-REIR) Algorithm Multi-Channel Impulse-Noise Removal by Impulse Classification (MC-IRIC) Multi-Channel Iterative Adaptive Switching Filter (MC-IASF) Multi-Channel Adaptive Local Thresholding with MAD (MC-ALT-MAD) Algorithm It is observed from the simulation results that the proposed filters perform better than the existing methods. The proposed methods: ASF-1 and IASF exhibit quite superior performance in suppressing SPN in high noise densities compared to other methods. Similarly ALT-MAD-3 exhibits much better performance in suppressing RVIN of low to medium noise densities.The REIR method is based on robust statistic technique, where adaptive window is used for pixel classification. The noisy pixel is replaced with Lorentzian estimator or average of the previously processed pixels. Because of adaptive windowing technique, the filter is able to suppress the noise at a density as high as 90%. In the proposed method, IDPSM, the noisy pixel is replaced with median of uncorrupted pixels in an adaptive filtering window. The iterative nature of the filter makes it more efficient in noise detection and adaptive filtering window technique makes it robust enough to preserve edges and fine details of an image in wide range of noise densities. The forth proposed method is IRIC. The noisy pixel is replaced with median of processed pixels in the filtering window. At high noise densities, the median filtering may not be able to reject outliers always. Under such circumstances, the processed left neighboring pixel is considered as the estimated output. The computational complexity of this method is equivalent to that of a median filter having a 3×3 window. The proposed algorithm requires simple physical realization structures. Therefore, this algorithm may be quite useful for online and real-time applications. Two different adaptive switching filters: ASF-I and ASF-II are developed for suppressing SPN at high noise density. The noisy pixel is replaced with alpha-trimmed mean value of uncorrupted pixels in the adaptive filtering window. Depending on noise estimation, a small filtering window size is initially selected and then the scheme adaptively changes the window size based on the number of noise-free pixels. Therefore, the proposed method removes the noise much more effectively even at noise density as high as 90% and yields high image quality. In the proposed method IASF, noisy pixel is replaced with alpha-trimmed mean value of uncorrupted pixels in the adaptive filtering window. Due to its iterative structure, the performance of this filter is better than existing order-statistic filters. Further, the adaptive filtering window makes it robust enough to preserve the edges and fine details of an image. Novel Restoration Techniques for Images Corrupted with High Density Impulsive Noise x The filters which are proposed for suppressing random-valued impulse noise (RVIN) are: Adaptive Window based Pixel-Wise MAD (AW-PWMAD) Algorithm Adaptive Local Thresholding with MAD (ALT-MAD) Algorithm The proposed method, Adaptive Window based Pixel-Wise MAD (AW-PWMAD) Algorithm is a modified MAD (Median of the Absolute Deviations from the median) scheme alongwith a threshold employed for pixel-classification. The noisy pixel is replaced with median of uncorrupted pixels in adaptive filtering window. Another proposed method for denoising the random-valued and fixed-valued impulse noise is ALT-MAD. A modified MAD based algorithm alongwith a local adaptive threshold is utilized for pixel-classification. The noisy pixel is replaced with median of uncorrupted pixels in the filtering window of adaptively varied size. Three threshold functions are suggested and employed in this algorithm. Thus, three different versions, namely, ALT-MAD-1, ALT-MAD-2 and ALT-MAD-3 are developed. They are observed to be quite efficient in noise detection and filtering. In the last part of the thesis, some efforts are made to develop filters for color image denoising. The filters which perform better in denoising gray-scale images are developed for suppression of impulsive noise from color images. Since the performance of denoising filters degrades in other color spaces, efforts are made to develop color image denoising filters in RGB color space only in this research work. The developed filters are: Multi-Channel Robust Estimator based Impulse-Noise Reduction (MC-REIR) Algorithm Multi-Channel Impulse-Noise Removal by Impulse Classification (MC-IRIC) Multi-Channel Iterative Adaptive Switching Filter (MC-IASF) Multi-Channel Adaptive Local Thresholding with MAD (MC-ALT-MAD) Algorithm It is observed from the simulation results that the proposed filters perform better than the existing methods. The proposed methods: ASF-1 and IASF exhibit quite superior performance in suppressing SPN in high noise densities compared to other methods. Similarly ALT-MAD-3 exhibits much better performance in suppressing RVIN of low to medium noise densities

An adaptive noise removal approach for restoration of digital images corrupted by multimodal noise

Data smoothing algorithms are commonly applied to reduce the level of noise and eliminate the weak textures contained in digital images. Anisotropic diffusion algorithms form a distinct category of noise removal approaches that implement the smoothing process locally in agreement with image features such as edges that are typically determined by applying diverse partial differential equation (PDE) models. While this approach is opportune since it allows the implementation of feature-preserving data smoothing strategies, the inclusion of the PDE models in the formulation of the data smoothing process compromises the performance of the anisotropic diffusion schemes when applied to data corrupted by non-Gaussian and multimodal image noise. In this paper we first evaluate the positive aspects related to the inclusion of a multi-scale edge detector based on the generalisation of the Di Zenzo operator into the formulation of the anisotropic diffusion process. Then, we introduce a new approach that embeds the vector median filtering into the discrete implementation of the anisotropic diffusion in order to improve the performance of the noise removal algorithm when applied to multimodal noise suppression. To evaluate the performance of the proposed data smoothing strategy, a large number of experiments on various types of digital images corrupted by multimodal noise were conducted.Keywords — Anisotropic diffusion, vector median filtering, feature preservation, multimodal noise, noise removal

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

Adaptive Image Restoration: Perception Based Neural Nework Models and Algorithms.

Abstract This thesis describes research into the field of image restoration. Restoration is a process by which an image suffering some form of distortion or degradation can be recovered to its original form. Two primary concepts within this field have been investigated. The first concept is the use of a Hopfield neural network to implement the constrained least square error method of image restoration. In this thesis, the author reviews previous neural network restoration algorithms in the literature and builds on these algorithms to develop a new faster version of the Hopfield neural network algorithm for image restoration. The versatility of the neural network approach is then extended by the author to deal with the cases of spatially variant distortion and adaptive regularisation. It is found that using the Hopfield-based neural network approach, an image suffering spatially variant degradation can be accurately restored without a substantial penalty in restoration time. In addition, the adaptive regularisation restoration technique presented in this thesis is shown to produce superior results when compared to non-adaptive techniques and is particularly effective when applied to the difficult, yet important, problem of semi-blind deconvolution. The second concept investigated in this thesis, is the difficult problem of incorporating concepts involved in human visual perception into image restoration techniques. In this thesis, the author develops a novel image error measure which compares two images based on the differences between local regional statistics rather than pixel level differences. This measure more closely corresponds to the way humans perceive the differences between two images. Two restoration algorithms are developed by the author based on versions of the novel image error measure. It is shown that the algorithms which utilise this error measure have improved performance and produce visually more pleasing images in the cases of colour and grayscale images under high noise conditions. Most importantly, the perception based algorithms are shown to be extremely tolerant of faults in the restoration algorithm and hence are very robust. A number of experiments have been performed to demonstrate the performance of the various algorithms presented